Big Data Vs Data Science Vs Data Analytics: A Detailed Comparison
Big Data Vs Data Science Vs Data Analytics: A Detailed Comparison

Big Data Vs Data Science Vs Data Analytics: A Detailed Comparison

Big Data Vs Data Science Vs Data Analytics Comparison

Big Data, Data Science and Data Analytics are interconnected disciplines that deal with data collection, processing, analysis and application. While they overlap in certain areas, there are also distinct differences between the three.

Understanding these nuances helps organizations maximize value by applying the right approach. This article provides an in-depth comparison between Big Data, Data Science and Data Analytics across key aspects.

Understanding Big Data

What is Big Data?

Big Data refers to extremely large, complex datasets that traditional data processing cannot handle. It focuses on ingesting, processing, storing and managing such high volume, velocity and variety of data.

Focus and Objectives of Big Data:

The core focus of Big Data is on strategies and technologies to collect, store, process and structure large amounts of data from diverse sources. Key goals are scalability, speed, reliability, and management of data pipelines.

Skills Required for Big Data:

Big data systems require skills like Hadoop, MapReduce, Spark, NoSQL databases, stream processing, distributed programming, and cloud infrastructure.

Tools and Technologies for Big Data

  1. Hadoop
  2. Apache Spark
  3. Apache Flink
  4. Apache Kafka
  5. Apache HBase
  6. Apache Cassandra
  7. Apache Hive
  8. Apache Pig
  9. Apache Drill
  10. Apache Sqoop
  11. Apache Zeppelin
  12. Hadoop Distributed File System (HDFS)
  13. MapReduce
  14. Docker
  15. Kubernetes
  16. Amazon EMR
  17. Amazon Redshift
  18. Amazon Kinesis
  19. Google BigQuery
  20. Google Dataproc
  21. Google Dataflow
  22. Microsoft Azure HDInsight
  23. Microsoft Azure Databricks
  24. Microsoft Azure Synapse Analytics
  25. Apache NiFi
  26. Elasticsearch

How Big Data Enables Business Success?

By supporting large-scale data management and analytics, Big Data allows deriving valuable business insights that were not possible earlier, leading to innovation and optimized operations.

Application Areas of Big Data

Big data powers a range of applications like targeted marketing, user behaviour analysis, fraud detection, operational optimization, risk modelling, and IoT data processing.

Understanding Data Science

What is Data Science?

Data Science combines domain expertise, programming skills and knowledge of math and statistics to extract meaningful insights from structured and unstructured data. It utilizes advanced techniques like machine learning and statistical modelling.

Focus and Objectives of Data Science:

Data Science aims to uncover hidden patterns, unknown correlations and other useful information from data using specialized skills in math, stats, programming, modelling, machine learning, and visualization.

Skills Required for Data Science:

Data scientists need capabilities in statistics, R/Python programming, ML/DL algorithms, math modelling, algorithms, predictive analysis, and visualization.

Tools and Technologies for Data Science

  1. Python
  2. R
  3. Jupyter Notebooks
  4. Anaconda
  5. TensorFlow
  6. PyTorch
  7. scikit-learn
  8. Pandas
  9. NumPy
  10. Matplotlib
  11. Seaborn
  12. Plotly
  13. SQL
  14. Apache Spark
  15. Hadoop
  16. Apache Flink
  17. Apache Kafka
  18. Apache Hive
  19. Tableau
  20. Excel
  21. Power BI
  22. SAS
  23. MATLAB
  24. KNIME
  25. RapidMiner
  26. D3.js
  27. NLTK (Natural Language Toolkit)
  28. spaCy
  29. Gensim
  30. StatsModels

How Data Science Enables Business Success?

Advanced analytics and ML models built by data scientists can uncover hidden patterns to detect fraud, predict demand, identify risks, recommend products, and drive growth.

Application Areas of Data Science

Data science is applied in diverse domains like predictive maintenance, healthcare, recommendation engines, image recognition, NLP, autonomous vehicles, and smart assistants like Alexa.

Understanding Data Analytics

What is Data Analytics?

Data Analytics refers to techniques used to analyze datasets and drive fact-based planning, decision-making and execution. It enables organizations to optimize processes and uncover information.

Related Blog: Data Analytics: What It Is, How It’s Used, Types, Feature’s, Tools and Techniques

(Note this hyperlink is for Blog 34 which is yet to be be published, so hyperlink accordingly)

Focus and Objectives of Data Analytics:

The main emphasis of Data Analytics is on analyzing data to derive insights that can drive business strategy, planning and decision-making. Key objectives are enabling data-driven actions and optimizing business outcomes.

Skills Required for Data Analytics:

Data analytics calls for SQL querying, data mining, dashboarding, business intelligence, statistics, communication, and an understanding of business domains.

Tools and Technologies for Data Analytics

  1. Microsoft Excel
  2. Tableau
  3. Google Analytics
  4. Power BI
  5. SAS
  6. SQL
  7. Python
  8. R
  9. Apache Hadoop
  10. Apache Spark
  11. Apache Hive
  12. Apache Pig
  13. Apache Kafka
  14. Apache Flink
  15. Apache NiFi
  16. IBM SPSS
  17. MATLAB
  18. KNIME
  19. RapidMiner
  20. QlikView/Qlik Sense
  21. Alteryx
  22. Domo
  23. Looker
  24. Splunk
  25. Grafana
  26. Kibana
  27. Mixpanel
  28. Adobe Analytics
  29. HBase
  30. MongoDB

How Data Analytics Enables Business Success?

Data analytics provides data-driven insights about customers, performance, trends, industry dynamics. This leads to data-informed decision making across the organization.

Related Blog: Why Is Data-Driven Analytics Of Interest To Companies?

Application Areas of Data Analytics

Data analytics has broad application across sales, marketing, finance, operations, supply chain, strategic planning and management to drive data-based actions.

Big Data Vs Data Science Vs Data Analytics: Comparison Table

ParameterBig DataData ScienceData Analytics
DefinitionCollecting and storing large, complex data from various sourcesUsing scientific processes and algorithms to extract insights from dataTechniques to analyze data to find actionable patterns and insights
FocusScalable data storage and managementAdvanced statistical modelling and ML algorithmsExploring data to inform decisions
SkillsData engineering, distributed systemsStatistical modelling, ML engineering, analyticsExploratory analysis, statistical methods
ToolsHadoop, NoSQL databases, SparkPython, R, Tensorflow, PyTorchTableau, Qlik, MATLAB, statistical software
RoleFoundation for large-scale analyticsDerive predictive insights from dataEnable data-driven decision-making
ImpactEnables complex analytics by managing volume, variety, velocity, veracity of dataBuilds models to uncover hidden insights and predict unknown eventsProvides metrics, trends, forecasts to optimize business outcomes
ApplicationsDigital marketing, social media, smart devicesFraud detection, medical diagnosis, sentiment analysis, recommendationsBusiness intelligence, financial forecasting, operational analytics

Commonalities Between Big Data, Data Science and Data Analytics

While having distinct focuses, Big Data, Data Science and Data Analytics also overlap in some aspects:

1. Shared Reliance on Data Processing and Analysis

All three rely on capabilities to store, process, clean, extract, analyze and interpret data to create business value. They form the core pillars of modern data management and analytics.

2. Contribution to Data-Driven Decision Making

These disciplines complement each other to enable data-driven decision-making through fact-based insights about the business, customer preferences, market dynamics, trends, and optimal strategies.

3. Integration in Business Operations

Leading companies integrate capabilities across big data, data science and analytics into their business operations, strategies, products and competitive positioning. They provide the data foundation.

Key Considerations for Implementation

Factors to consider when implementing big data, data science and analytics:

  • Clear business objectives, use cases and ROI
  • Integrated platform vs siloed solutions
  • In-house team vs external consultants
  • Hybrid and multi-cloud deployment
  • Data governance, security and ethics
  • Change management and cultural adoption
  • Future scalability and flexibility
  • Timeline, total cost of ownership and ROI

FAQs About Big Data, Data Science and Data Analytics

Q1. How do Big Data, Data Science, and Data Analytics relate to each other?

Big Data provides the infrastructure to store and process large amounts of data. Data Science analyzes this data for patterns. Data Analytics evaluates insights to drive business decisions.

Q2. What are some real-world applications of each discipline?

Big Data enables Netflix recommendations, Twitter trend analysis, and IoT data. Data Science powers fraud prediction, disease diagnosis, and chatbots. Data analytics drives business intelligence, marketing analytics, and operational optimization.

Q3. What are the primary goals of each discipline?

Big Data aims to store and process large, complex data. Data Science seeks to uncover new predictive insights. Data Analytics focuses on enabling data-backed business decisions.

Q4. What are the main challenges faced in each discipline?

Big Data faces issues around real-time processing, security, and siloed data. Data Science struggles with model interpretability, and overfitting. Data Analytics has adoption and data quality challenges.

Q5. What are some common misconceptions?

Big Data doesn’t always imply value without analytics. Data Science is more than just modelling. Analytics is not just visualizations but applying insights.


While Big Data, Data Science and Analytics have distinct scopes, they work best when integrated together. Organizations must assess their maturity, objectives and use cases to determine the right approach. With the exponential growth of data, leveraging these in combination can unlock immense value.

Ready to Harness Your Data Potential? Let BuzzyBrains Lead the Way!

BuzzyBrains helps global organizations design and implement Big Data, Data Science and Analytics capabilities tailored to their specific business context and objectives. Contact us today to start your data journey! Our end-to-end data services, proven methodologies, and domain expertise empower companies to convert data into strategic business assets.

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